A term frequency-inverse document frequency (TF-IDF) weight may be used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the corpus. Variations of the TF-IDF weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. Lists of entities contain information grouped according to some criterion. As such, lists are a good source of information to determine relevant information responsive to a query. However, entities may occur in different lists and may be associated with different members in each list. In addition, there are a large number of lists on the web and assigning weights to such a large number of lists creates hurdles in mining such lists for information.